Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences
- Josh T. Cuperus1,2,7,
- Benjamin Groves3,7,
- Anna Kuchina3,7,
- Alexander B. Rosenberg3,7,
- Nebojsa Jojic4,
- Stanley Fields1,2,5 and
- Georg Seelig3,6
- 1Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA;
- 2Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA;
- 3Department of Electrical Engineering, University of Washington, Seattle, Washington 98195, USA;
- 4Microsoft Research, Seattle, Washington 98195, USA;
- 5Department of Medicine, University of Washington, Seattle, Washington 98195, USA;
- 6Department of Computer Science & Engineering, University of Washington, Seattle, Washington 98195, USA
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↵7 These authors contributed equally to this work.
Abstract
Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5′ untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5′ UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5′ UTRs as well as native S. cerevisiae 5′ UTRs. The model additionally was used to computationally evolve highly active 5′ UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.224964.117.
- Received May 12, 2017.
- Accepted October 18, 2017.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
